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Spatial downscaling of land surface temperature with the multi-scale geographically weighted regression

  • role： First author第一作者
  • Affiliation：

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 101408, China

    Yanshan Earth Critical Zone and Surface Fluxes Research Station, University of Chinese Academy of Sciences, Beijing 101408, China

  • Email：zhuxinming19@mails.ucas.ac.cn
  • Introduction：祝新明，1992年生，男，博士研究生，研究方向为地表温度降尺度、微波遥感地表温度反演。E-mail： zhuxinming19@mails.ucas.ac.cn
ZHU Xinming12,  
  • role： Corresponding author通信作者
  • Affiliation：

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 101408, China

    Yanshan Earth Critical Zone and Surface Fluxes Research Station, University of Chinese Academy of Sciences, Beijing 101408, China

  • Email：songxn@ucas.edu.cn
  • Introduction：宋小宁，1975年生，女，教授，研究方向为定量遥感，土壤湿度反演。E-mail： songxn@ucas.edu.cn
SONG Xiaoning12*,  
  • Affiliation：

    Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China

LENG Pei3,  
  • Affiliation：

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 101408, China

    Yanshan Earth Critical Zone and Surface Fluxes Research Station, University of Chinese Academy of Sciences, Beijing 101408, China

HU Ronghai12

résumé

Due to the limitations of thermal infrared technology, a single sensor cannot provide both high frequency and fine spatial resolution Land Surface Temperature (LST) data. For solving this problem, it becomes an effective way by conducting the spatial downscaling of LST product with low-resolution and high frequency in collaboration with other auxiliary data. However, the existing LST downscaling methods do not fully consider the scale effects of different biophysical parameters on the distribution of LST, which makes the accuracy and spatial distribution of the downscaled LST are inconsistent. In view of this, taking Beijing and Zhangye as two study areas, this paper proposed a kind of LST downscaling algorithm to sharpen the MODIS LST using Multi-scale Geographically Weighted Regression (MGWR) according to the analyse of effects of NDVI, DEM, slope, latitude, and longitude on LST heterogeneity. Furthermore, four kinds of LST downscaling methods (i.e., TsHARP algorithm, ML algorithm, GWR algorithm, and RF algorithm) were introduced in this paper for further comparison and validation. Results show that the constructed LST conversion function based on the MGWR reveals the actual interaction between various scale factors and LST at various spatial scales. NDVI and slope have global impacts on the LST, while DEM and geolocation present local impacts on the LST. Compared with the four referenced methods, the downscaled 100 m resolution LST based on the MGWR has better spatial textures and displays clear landscape features in heterogeneous areas such as deserts and towns. In addition, all images predicted by the MGWR algorithm showed better accuracy, in which the area proportion under the 0—1 K error level were all more than 57%, the root-mean-square error (RMSE) were all less than 2.85 K, and the coefficient of determination (R2) were all more than 0.88.

mots-clés

Land Surface Temperature (LST);spatial downscaling;MGWR;scale difference;MODIS

References

  1. 1.
    Agam N, Kustas W P, Anderson M C, Li F Q and Neale C M U. 2007. A vegetation index based technique for spatial sharpening of thermal imagery. Remote Sensing of Environment, 107 (4): 545-558
  2. 2.
    Dominguez A, Kleissl J, Luvall J C and Rickman D L. 2011. High-resolution urban thermal sharpener (HUTS). Remote Sensing of Environment, 115 (7): 1772-1780
  3. 3.
    Duan S B and Li Z L. 2016. Spatial downscaling of MODIS land surface temperatures using geographically weighted regression case study in Northern China. IEEE Transactions on Geoscience and Remote Sensing, 54 (11): 6458-6469
  4. 4.
    Duan S B, Li Z L and Leng P. 2017. A framework for the retrieval of all-weather land surface temperature at a high spatial resolution from polar-orbiting thermal infrared and passive microwave data. Remote Sensing of Environment, 195: 107-117
  5. 5.
    Duan S B, Li Z L, Li H, Göttsche F M, Wu H, Zhao W, Leng P, Zhang X and Coll C. 2019. Validation of Collection 6 MODIS land surface temperature product using in situ measurements. Remote Sensing of Environment, 225: 16-29
  6. 6.
    Duan S B, Ru C, Li Z L, Wang M M, Xu H Q, Li H, Wu P H, Zhan W F, Zhou J, Zhao W, Ren H Z, Wu H, Tang B H, Zhang X, Shang Guo F and Qin Z H. 2021. Reviews of methods for land surface temperature retrieval from Landsat thermal infrared data. National Remote Sensing Bulletin, 25(8): 1591-1617
  7. 7.
    Fotheringham A S, Brunsdon C and Charlton M. 2002. Geographically weighted regression: the analysis of spatially varying relationships. Chichester/Hoboken Wiley.
  8. 8.
    Fotheringham A S, Yang W and Kang W. 2017. Multiscale geographically weighted regression (MGWR). Annals of the American Association of Geographers, 107 (6): 1247-1265
  9. 9.
    Gao F, Masek J, Schwaller M and Hall F. 2006. On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 44 (8): 2207-2218
  10. 10.
    Gu H Y, Meng X, Shen T Y and Cui N N. Spatial variation of the determinants of China's urban floating population's settlement intention. Acta Geographica Sinica, 75 (2): 240-254
  11. 11.
    Hua J W, Zhu S Y and Zhang G X. 2018. Downscaling land surface temperature based on random forest algorithm. Remote Sensing for Land and Resources, 2018, 30 (1): 78-86
  12. 12.
    Huang C, Duan S B, Jiang X G, Han X L, Leng P, Gao M F and Li Z L. 2018. A physically based algorithm for retrieving land surface temperature under cloudy conditions from AMSR2 passive microwave measurements. International Journal of Remote Sensing, 40 (5-6): 1828-1843
  13. 13.
    Hutengs C and Vohland M. 2016. Downscaling land surface temperatures at regional scales with random forest regression. Remote Sensing of Environment, 178: 127-141
  14. 14.
    Kustas W P, Norman J M, Anderson M C and French A N. 2003. Estimating subpixel surface temperatures and energy fluxes from the vegetation index–radiometric temperature relationship. Remote Sensing of Environment, 85 (4): 429-440
  15. 15.
    Li W, Ni L, Li Z L, Duan S B and Wu H. 2019. Evaluation of machine learning algorithms in spatial downscaling of MODIS land surface temperature. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12 (7): 2299-2307
  16. 16.
    Li X J, Jiang T, Xin X Z, Zhang H L and Liu Q H. 2016. Spatial downscaling of land surface temperature based on MODIS data. Chinese Journal of Ecology, 2016, 35 (12): 3443-3450
  17. 17.
    Li Z L, Duan S B, Tang B H, Wu H, Ren H Z, Yan G J, Tang R L and Leng P. 2016. Review of methods for land surface temperature derived from thermal infrared remotely sensed data. Journal of Remote Sensing, 20 (5): 899-920
  18. 18.
    Li Z L, Tang B H, Tang R L, Wu H, Duan S B, Leng P and Zhang R H. 2017. Theory and method of thermal infrared remote sensing inversion of land surface temperature. Science Focus, 12 (6): 57-69
  19. 19.
    Liu M, Tang R L, Li Z L, Gao M F and Yao Y J. 2021. Progress of data-driven remotely sensed retrieval methods and products on land surface evapotranspiration. National Remote Sensing Bulletin, 25(8): 1517-1537
  20. 20.
    Mukherjee S, Joshi P K and Garg R D. 2014. A comparison of different regression models for downscaling Landsat and MODIS land surface temperature images over heterogeneous landscape. Advances in Space Research, 54 (4): 655-669
  21. 21.
    Nie J L, Wu J J, Yang X, Liu M, Zhang J and Zhou L. Downscaling land surface temperature based on relationship between surface temperature and vegetation index. Acta Ecologica Sinica, 2011, 31 (17): 4961-4969
  22. 22.
    Oke T R. 1982. The energetic basis of the urban heat island. Royal Meteorological Society, 108 (455): 1-24
  23. 23.
    Oshan T M, Li Z Q, Kang W, Wolf L J and Fotheringham A S. 2019. Mgwr: a python implementation of multiscale geographically weighted regression for investigating process spatial heterogeneity and scale. International Journal of Geo-Information, 8 (269): 1-31
  24. 24.
    Peng X, Wu W, Zheng Y, Sun J, Hu T and Wang P. 2020. Correlation analysis of land surface temperature and topographic elements in Hangzhou, China. Scientific Reports, 10 (1): 1-16
  25. 25.
    Peng Y D, Li W S, Luo X B and Li H. 2019. A geographically and temporally weighted regression model for spatial downscaling of MODIS land surface temperatures over urban heterogeneous regions. IEEE Transactions on Geoscience and Remote Sensing, 57 (7): 5012-5027.
  26. 26.
    Qin Z H, Karnieli A and Berliner P. 2001. A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region. International journal of remote sensing, 22 (18): 3719-3746
  27. 27.
    Qin Z H, Li W J, Xu B, Chen Z X and Liu J. 2004. The estimation of land surface emissivity for Landsat TM6. Remote Sensing for Land and Resources, 16 (3): 28-32
  28. 28.
    Quan J L, Zhan W F, Chen Y H and Liu W Y. 2013. Downscaling remotely sensed land surface temperatures: A comparison of typical methods. Journal of Remote Sensing, 17 (2): 361-387
  29. 29.
    Shen T Y, Yu H C, Zhou L, Gu H Y and He H H. 2020. On hedonic price of second-hand houses in Beijing based on multi-scale geographically weighted regression: scale law of spatial heterogeneity. Economic Geography, 40 (3): 75-83
  30. 30.
    Voogt J A and Oke T R. 2003. Thermal remote sensing of urban climates. Remote Sensing of Environment, 86 (3): 370-384
  31. 31.
    Wan Z M and Li Z L. 2008. Radiance-based validation of the V5 MODIS land-surface temperature product. International journal of remote sensing, 29 (17): 5373-5395
  32. 32.
    Wang S M, Luo X B and Peng Y D. 2020. Spatial downscaling of MODIS land surface temperature based on geographically weighted autoregressive model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 1-15
  33. 33.
    Wang Y T, Xie D H and Li Y H. 2014. Downscaling remotely sensed land surface temperature over urban areas using trend surface of spectral index. Journal of Remote Sensing, 18 (6): 1169-1181
  34. 34.
    Wang Z H, Qin Q M, Sun Y H, Zhang T Y and Ren H Z. 2018. Downscaling remotely sensed land surface temperature with the BP neural network. Remote Sensing Technology and Application, 2018, 33 (5): 793-802
  35. 35.
    Wei R and Shan J. 2018. Spatial and temporal fusion for urban land surface temperature image mapping. Geomatics and Information Science of Wuhan University, 43 (3): 428-435
  36. 36.
    Weng Q H. 2009. Thermal infrared remote sensing for urban climate and environmental studies: methods, applications, and trends. ISPRS Journal of Photogrammetry and Remote Sensing, 64 (4): 335-344
  37. 37.
    Weng Q H, Fu P and Gao F. 2014. Generating daily land surface temperature at Landsat resolution by fusing Landsat and MODIS data. Remote Sensing of Environment, 145: 55-67
  38. 38.
    Wu P H, Shen H F, Zhang L P and Göttsche F M. 2015. Integrated fusion of multi-scale polar-orbiting and geostationary satellite observations for the mapping of high spatial and temporal resolution land surface temperature. Remote Sensing of Environment, 156: 169-181
  39. 39.
    Wu P H, Yin Z X, Chao Z, Duan S B, Gottsche F M, Ma X S, Li X H, Yang H and Shen H F. 2021. Spatially continuous and high-resolution land surface temperature: a review of reconstruction and spatiotemporal fusion techniques. Electrical Engineering and Systems Science, 1-42
  40. 40.
    Yang G J, Pu R L, Huang W J, Wang J H and Zhao C J. 2010. A novel method to estimate subpixel temperature by fusing solar-reflective and thermal-infrared remote-sensing data with an artificial neural network. IEEE Transactions on Geoscience and Remote Sensing, 48 (4): 2170-2178
  41. 41.
    Yang Y B, Cao C, Pan X, Li X L and Zhu X. 2017. Downscaling land surface temperature in an arid area by using multiple remote sensing indices with Random Forest regression. Remote Sensing, 9 (789): 1-18
  42. 42.
    Yin Z X, Wu P H, Foody G M, Liu Z H, Du Y and Ling F. 2020. Spatiotemporal fusion of land surface temperature based on a convolutional neural network. IEEE Transactions on Geoscience and Remote Sensing, PP (99): 1-15
  43. 43.
    Yoo C, Im J, Park S and Cho D. 2020. Spatial downscaling of MODIS land surface temperature: recent research trends, challenges, and future directions. Korean Journal of Remote Sensing, 36 (4): 609-626
  44. 44.
    Yu H C, Fotheringham A S, Li Z Q, Oshan T, Kang W and Wolf L J. 2019. Inference in multiscale geographically weighted regression. Geographical Analysis, 52 (1): 87-106
  45. 45.
    Zhan W F, Chen Y H, Wang J F, Zhou J, Quan J L, Liu W Y and Li J. 2012. Downscaling land surface temperatures with multi-spectral and multi-resolution images. International Journal of Applied Earth Observations and Geoinformation, 18: 23-36
  46. 46.
    Zhan W F, Chen Y H, Zhou J, Li J and Liu W Y. 2011. Sharpening thermal imageries: a generalized theoretical framework from an assimilation perspective. IEEE Transactions on Geoscience and Remote Sensing, 49 (2): 773-789
  47. 47.
    Zhang Q, Wang N, Cheng J and Xu S. 2020. A stepwise downscaling method for generating high-resolution land surface temperature from AMSR-E data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13: 5669-5681
  48. 48.
    Zhao W and Duan S B. 2020. Reconstruction of daytime land surface temperatures under cloud-covered conditions using integrated MODIS/Terra land products and MSG geostationary satellite data. Remote Sensing of Environment 247, 111931
  49. 49.
    Zhu S Y, Guan H D, Millington A C and Zhang G X. 2013. Disaggregation of land surface temperature over a heterogeneous urban and surrounding suburban area: a case study in Shanghai, China. International Journal of Remote Sensing, 34 (5-6): 1707-1723
  50. 50.
    Zhu X L, Cai F Y, Tian J Q and Trecia W. 2018. Spatiotemporal fusion of multisource remote sensing data: literature survey, taxonomy, principles, applications, and future directions. Remote Sensing, 10 (4): 1-23
  51. 51.
    Zhu X L, Chen J, Gao F, Chen X H and Masek J G. 2010. An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions. Remote Sensing of Environment, 114 (11): 2610-2623
  52. 52.
    Zhu X M, Song X N, Leng P, Guo D and Cai S H. 2020. Impact of atmospheric correction on spatial heterogeneity relations between land surface temperature and biophysical compositions. IEEE Transactions on Geoscience and Remote Sensing, 59 (3): 2680-2697
  53. 53.
    Zhu X M, Wang X H, Yan D J, Liu Z and Zhou Y F. 2019. Analysis of remotely-sensed ecological indexes' influence on urban thermal environment dynamic using an integrated ecological index: a case study of Xi'an, China. International Journal of Remote Sensing, 40 (9): 3421-3447
  54. 54.
    Zhu X M, Wang X H, Zhou Y F, Wu W H and Liu Z. 2017. Spatial variability of thermal environment in Xi'an under the build-up area expansion. Chinese Journal of Ecology, 36 (12): 3574-3583

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